1. Field of Invention
This invention relates to the automatic generation of superwords based on a criterion relevant to speech recognition.
2. Description of Related Art
In the art of speech recognition, a speech recognizer is used to extract recognized words from a given speech utterance. Typically, a large vocabulary recognizer is constrained by stochastic language models such as an n-gram model. One approach to such modeling to constrain the recognizer is to train a stochastic finite state grammar represented by a Variable Ngram Stochastic Automaton (VNSA). A VNSA is a non-deterministic automaton that allows for parsing any possible sequence of words drawn from a given vocabulary.
Traditionally, such n-gram language models for speech recognition assume words as the basic lexical unit. The order of a VNSA network is the maximum number of words that can be predicted as occurring after the occurrence of a particular word in an utterance. Thus, using conditional probabilities, VNSAs have been used to approximate standard n-gram language models yielding similar performance to standard bigram and trigram models. However, when the "n" in the n-gram becomes large, a database for predicting the occurrence of words in response to the appearance of a word in an utterance, becomes large and unmanageable. In addition, the occurrence of words which are not strongly recurrent in the language may be mistakenly assigned high probabilities, and thus generate a number of misdetections in recognized speech.
Thus, a method to create longer units for language modeling is needed in order to promote the efficient use of n-gram language models for speech recognition.